Hugging Face MCP Server

The Hugging Face MCP Server implements the Model Context Protocol and connects MCP-capable AI assistants to the Hugging Face Hub: search models, datasets, Spaces, and research papers, query Hugging Face documentation via natural-language search, and call MCP-compatible Gradio applications (community tools) directly from the chat. The built-in hf_fs tool efficiently covers most Hub tasks, including semantic search across documents and Spaces.
Additional tools can be enabled from the MCP settings page: write access to your own repositories, sandboxes with file management, and running and managing jobs on Hugging Face infrastructure. These extended tools require signing in with a Hugging Face account.

Hosted option (recommended)

Hugging Face runs the server as a hosted remote service at https://huggingface.co/mcp. The corresponding settings page lets you pick your client (for example Claude Code, Claude Desktop, Cursor, VS Code, Zed, Gemini CLI, or ChatGPT); the page then generates a ready-to-paste configuration. Tools with write access, sandboxes, or jobs require signing in with a Hugging Face account.

Local option

Alternatively, the open-source package @llmindset/hf-mcp-server can be run locally via npx, in either STDIO or stateless Streamable HTTP mode. A Docker image is available at ghcr.io/evalstate/hf-mcp-server. Both options are useful when traffic should not go through the hosted service.

Community Spaces as a distinguishing feature

What sets the Hugging Face MCP Server apart from most other MCP servers is its connection to community Spaces: instead of only offering a fixed set of tools, any MCP-compatible Gradio application published on the Hub can be called as an additional tool in the chat. This opens the server up to a constantly growing number of specialized tools, from image editing to text analysis to domain-specific models, without Hugging Face having to implement every single feature itself. The trade-off is that these tools aren't vetted by Hugging Face itself.

Typical use cases

The server is particularly useful for research work in the machine learning space: finding suitable pretrained models for a specific task, searching datasets by size and license, looking up recent research papers on a topic, or trying out a community tool directly without switching context to a browser. For data science teams that regularly evaluate new models, natural-language Hub search can be noticeably faster than manually clicking through the web interface.

Frequently asked questions

Are all community Spaces trustworthy? No, they come from third parties and aren't vetted by Hugging Face itself; it's worth checking the operator before use. Does basic usage require a Hugging Face account? No, simple Hub searches and documentation queries work without signing in. Can I use the server entirely without Hugging Face infrastructure? No, even the local option still communicates with Hugging Face infrastructure for Hub data.

For the hosted option, an MCP client that supports remote servers (the settings page shows client-specific instructions); extended tools (write access, sandboxes, jobs) additionally require a signed-in Hugging Face account. For the local option, Node.js (npx) or Docker.

For the hosted option, open the MCP settings page at https://huggingface.co/mcp while signed in, pick your client, and paste the generated configuration into the client's MCP settings, then restart the client.

For the local option, start the server via npx:

npx @llmindset/hf-mcp-server        # STDIO mode
npx @llmindset/hf-mcp-server-http   # Streamable HTTP (stateless)

Alternatively, using Docker:

docker pull ghcr.io/evalstate/hf-mcp-server:latest
docker run --rm -p 3000:3000 ghcr.io/evalstate/hf-mcp-server:latest

Basic search and documentation tools work without signing in. Extended tools (write access to repositories, sandboxes, jobs) require a signed-in Hugging Face account. HTTP clients send a Hugging Face token via the Authorization: Bearer header; the local STDIO option falls back to HF_TOKEN or DEFAULT_HF_TOKEN.

Without signing in, only read-only Hub searches and documentation queries are possible. When signed in, scope depends on which tools are enabled in the MCP settings: by default only Hub navigation is active, with optional write access to your own repositories, sandboxes, or running jobs on Hugging Face infrastructure.

Search queries and tool parameters go to Hugging Face infrastructure and are returned to the MCP client and its language model to answer the request. When calling community Spaces as tools, requests additionally pass through the respective third-party-operated Gradio application. With write or job tools enabled, the service additionally processes content from your own Hugging Face repositories and jobs.

Community Spaces embedded as tools come from third parties and are not vetted by Hugging Face itself; a malicious or faulty Space can return misleading results or misuse inputs. With write access enabled, an agent can modify your own repositories; with job tools enabled, it can consume compute time on your own infrastructure. Enable only the Spaces and tools you need, turn on write and job access deliberately, and treat the Hugging Face token like a password.